WO2010033521A3 - Methods for enabling a scalable transformation of diverse data into models to drive the discovery of new knowledge - Google Patents

Methods for enabling a scalable transformation of diverse data into models to drive the discovery of new knowledge Download PDF

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Publication number
WO2010033521A3
WO2010033521A3 PCT/US2009/057046 US2009057046W WO2010033521A3 WO 2010033521 A3 WO2010033521 A3 WO 2010033521A3 US 2009057046 W US2009057046 W US 2009057046W WO 2010033521 A3 WO2010033521 A3 WO 2010033521A3
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WO
WIPO (PCT)
Prior art keywords
data
drive
models
discovery
enabling
Prior art date
Application number
PCT/US2009/057046
Other languages
French (fr)
Other versions
WO2010033521A2 (en
Inventor
Akhileswar Ganesh Vaidyanathan
Stephen D. Prior
Jijun Wang
Bin Yu
Original Assignee
Quantum Leap Research, Inc.
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Quantum Leap Research, Inc. filed Critical Quantum Leap Research, Inc.
Publication of WO2010033521A2 publication Critical patent/WO2010033521A2/en
Publication of WO2010033521A3 publication Critical patent/WO2010033521A3/en

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Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/12—Computing arrangements based on biological models using genetic models
    • G06N3/126—Evolutionary algorithms, e.g. genetic algorithms or genetic programming
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
    • G06F18/211—Selection of the most significant subset of features
    • G06F18/2115—Selection of the most significant subset of features by evaluating different subsets according to an optimisation criterion, e.g. class separability, forward selection or backward elimination
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/60—Type of objects
    • G06V20/69—Microscopic objects, e.g. biological cells or cellular parts
    • G06V20/698—Matching; Classification
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/50—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for simulation or modelling of medical disorders
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00—Computing arrangements using knowledge-based models
    • G06N5/01—Dynamic search techniques; Heuristics; Dynamic trees; Branch-and-bound
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N7/00—Computing arrangements based on specific mathematical models
    • G06N7/01—Probabilistic graphical models, e.g. probabilistic networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V2201/00—Indexing scheme relating to image or video recognition or understanding
    • G06V2201/03—Recognition of patterns in medical or anatomical images
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/70—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H70/00—ICT specially adapted for the handling or processing of medical references
    • G16H70/60—ICT specially adapted for the handling or processing of medical references relating to pathologies

Abstract

The present invention relates to a method for the automatic identification of at least one informative data filter from a data set that can be used to identify at least one relevant data subset against a target feature for subsequent hypothesis generation, model building and model testing. The present invention describes methods, and an initial implementation, for efficiently linking relevant data both within and across multiple domains and identifying informative statistical relationships across this data that can be integrated into agent-based models. The relationships, encoded by the agents, can then drive emergent behavior across the global system that is described in the integrated data environment.
PCT/US2009/057046 2008-09-16 2009-09-15 Methods for enabling a scalable transformation of diverse data into hypotheses, models and dynamic simulations to drive the discovery of new knowledge WO2010033521A2 (en)

Applications Claiming Priority (6)

Application Number Priority Date Filing Date Title
US9751208P 2008-09-16 2008-09-16
US61/097,512 2008-09-16
US21898609P 2009-06-21 2009-06-21
US61/218,986 2009-06-21
US12/556,591 2009-09-10
US12/556,591 US20120004893A1 (en) 2008-09-16 2009-09-10 Methods for Enabling a Scalable Transformation of Diverse Data into Hypotheses, Models and Dynamic Simulations to Drive the Discovery of New Knowledge

Publications (2)

Publication Number Publication Date
WO2010033521A2 WO2010033521A2 (en) 2010-03-25
WO2010033521A3 true WO2010033521A3 (en) 2010-05-20

Family

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PCT/US2009/057046 WO2010033521A2 (en) 2008-09-16 2009-09-15 Methods for enabling a scalable transformation of diverse data into hypotheses, models and dynamic simulations to drive the discovery of new knowledge

Country Status (2)

Country Link
US (1) US20120004893A1 (en)
WO (1) WO2010033521A2 (en)

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US10762111B2 (en) 2017-09-25 2020-09-01 International Business Machines Corporation Automatic feature learning from a relational database for predictive modelling
US11177024B2 (en) * 2017-10-31 2021-11-16 International Business Machines Corporation Identifying and indexing discriminative features for disease progression in observational data
US11281995B2 (en) 2018-05-21 2022-03-22 International Business Machines Corporation Finding optimal surface for hierarchical classification task on an ontology
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US10970272B2 (en) 2019-01-31 2021-04-06 Sap Se Data cloud—platform for data enrichment
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US11705226B2 (en) * 2019-09-19 2023-07-18 Tempus Labs, Inc. Data based cancer research and treatment systems and methods
CN110569543B (en) * 2019-08-02 2023-08-15 中国船舶工业系统工程研究院 Complex system self-adaption method and system supporting mapping dimension lifting
US11782401B2 (en) 2019-08-02 2023-10-10 Aspentech Corporation Apparatus and methods to build deep learning controller using non-invasive closed loop exploration
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US20120004893A1 (en) 2012-01-05
WO2010033521A2 (en) 2010-03-25

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